Optimizing operational performance through industry 4.0: a comprehensive model for data reliability and OEE enhancement

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The introduction of Industry 4.0 signifies a groundbreaking shift, integrating state-of-the-art technologies into manufacturing processes and propelling industries towards heightened efficiency and competitiveness. This article deals with the crucial role of productivity measurement in SMEs and examines the impact of data reliability on operational performance assessment. It explores the strategic use of Industry 4.0 tools to enhance data reliability in processes like production, quality, and maintenance. The research focuses on designing a comprehensive model for data collection, reliability, and utilization, ultimately aiming to improve Overall Equipment Effectiveness (OEE) within SMEs. By showcasing the synergistic integration of Industry 4.0 advancements, the article provides practical insights for SME stakeholders to optimize operational performance. The proposed model contributes to the understanding and implementation of efficient methodologies for data management, fostering sustainable improvements using calculation of OEE within SMEs. The case study was conducted in a plastics manufacturing SME that produces components for various industries. These findings can be enhanced and improved through additional case studies to refine the proposed model. Industry 4.0 SMEs OEE Industrial Performance Plastics Industry Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction The advent of Industry 4.0 marks a revolutionary paradigm shift, integrating cutting-edge technologies into manufacturing processes, thereby propelling industries towards heightened efficiency and competitiveness. Concurrently, the Overall Equipment Effectiveness (OEE) metric emerges as a vital tool, serving as a compass for enterprises seeking to optimize their machinery performance and operational processes. Additionally, recognizing the crucial role played by industrial data in the modern business ecosystem, the efficient harnessing and utilization of digital information in SMEs stand as a cornerstone for informed decision-making and improved organizational efficacy. This exploration delves into the synergy between Industry 4.0, OEE, and the efficient management of data, unraveling the transformative potential they collectively hold for the thriving SME sector. Otherwise, this fourth revolution and the resulting changes in process management have a very significant impact on the meaning and method of obtaining digital data. The principles of Industry 4.0 are already based on transparency and clarity of data throughout the process. Additionally, the assumptions of modern management methods (Lean) influence the need for changes in creating value by minimizing losses in operational areas that do not add value and ensuring transparency in operations that do add value. It seems particularly challenging to introduce changes (in line with 21st-century trends) in small and medium-sized enterprises (SMEs) in the manufacturing sector, as SMEs show a low level of maturity in the field of management [1]. Our article aims to deepen our understanding of the central role played by productivity measurement within small and medium-sized enterprises (SMEs). We strive to comprehensively break down the impact of data reliability on the accurate assessment of operational performance within these entrepreneurial structures. A prominent aspect of this research involves exploring how the innovative tools offered by Industry 4.0 can be judiciously utilized to enhance the reliability of data stemming from various processes such as production, quality, and maintenance. In this context, we delve into the design and implementation of a comprehensive model for data collection, data reliability, and data utilization. The ultimate goal of this model is to establish a robust methodology for improving Overall Equipment Effectiveness (OEE) within SMEs. We aim to demonstrate how Industry 4.0 advancements can be synergistically integrated into these processes to ensure increased data quality and reliability. By exploring these innovative avenues, we aspire to provide SME stakeholders with practical insights and informed recommendations on how to best leverage Industry 4.0 technologies to optimize their operational performance. In summary, our approach is geared towards making tangible contributions to the understanding and successful implementation of efficient methodologies for data collection, reliability, and utilization, thereby contributing to the sustainable improvement of OEE within SMEs. 2. Literature Review Industry 4.0 Dynamics Industry 4.0 sets the stage for a comprehensive societal and technological transformation, reshaping the global landscape significantly. Information is seamlessly integrated into the components, allowing for tasks such as ordering missing parts and configuring individual production parameters. Concurrently, clients are continuously informed about the latest production status. As the plant commences operations, a wealth of additional data is generated. Precise output and real-time performance data of the products can be collected, analyzed, and fed back into the development process. In this context, Industry 4.0 technologies play a pivotal role in advancing and optimizing both new technologies and processes. In Industry 4.0, business management relies, to some extent, on monitoring and analyzing collected data. Key components of smart manufacturing include processes, human/machine interactions, and the transition from paper to digital data. The primary objective is to establish a digital interaction mechanism for human-to-human, human-to-object, and object-to-object communication throughout the entire production process [2]. Other researchers present a model application tailored for small and medium-sized enterprises (SMEs), providing a comprehensive overview of existing Industry 4.0 concepts. Concurrently, Müller suggests associating business model implications with Industry 4.0, utilizing the Business Model Canvas as a reference. Elements such as key resources and value propositions are identified as the most significantly influenced components of the business model, while channels are noted as being less affected [3]. Rauch et al. model supports SMEs in formulating an individual strategy for the successful implementation of Industry 4.0 [4]. In today's manufacturing landscape, production companies face a crucial mandate for both operational excellence and flexibility in their manufacturing and assembly operations. This imperative arises from the ongoing shift from mass production to mass customization [5]. The evaluation of industrial system effectiveness, encompassing processes and machinery, has a well-established history and remains a central focus in recent research [6]. Considerable attention has also been devoted to exploring the flexibility of manufacturing equipment and its interconnectedness in achieving overall manufacturing flexibility. Current research is dedicated to clarifying concepts, operationalization, measurement frameworks, and related aspects [7]. Challenges and Opportunities The discourse surrounding "Industry 4.0" and the broader digitization process revolves around internal discussions concerning the technological challenges and opportunities presented by recent advancements. There is also significant consideration given to the direct and indirect impacts on employment, encompassing both quantitative and qualitative aspects, as well as on labor conditions. In recent years, two distinct narratives have surfaced. From a firm-level perspective and grounded in managerial discussions, the narrative of "emerging opportunities" suggests that digitalization offers new possibilities for companies. This perspective envisions firms becoming more agile and intelligent, reducing inefficiencies, fostering collaborative working systems, and optimizing inter-organizational relations within what is termed 'industrial ecosystems' [10]. As emphasized by Cirillo et al., an opposing perspective arises from a reading that underscores the risks associated with the widespread digitalization and interconnection of processes [11]. These risks include the reinforcement of decision-making power without the centralization of production [12], the resurgence of neo-Taylorization in work processes through the introduction of micromanagement practices and new forms of proceduralization characterized by extensive surveillance systems [13,14], and the unequal distribution of power and information [15]. Industry 4.0: Data Transformation Challenges The emergence of Industry 4.0 and its consequential changes in process management significantly impact the interpretation and utilization of digital data. The principles of Industry 4.0 are inherently grounded in ensuring transparency and clarity of data throughout the entire process. Additionally, contemporary management methodologies, such as Lean principles, contribute to the imperative of transforming the value creation process. This transformation involves minimizing losses in non-value-added operations and enhancing the transparency of operations that contribute value. Implementing these changes, in line with 21st-century trends, presents a specific challenge for small and medium-sized enterprises (SMEs) in the manufacturing sector. This challenge is exacerbated by the relatively low level of managerial maturity exhibited by SMEs in this domain [8]. The adoption of innovative technologies presents a challenge for small and medium-sized enterprises (SMEs), given their inherent weakness in handling complex procedures. This challenge is particularly evident in the context of Industry 4.0, where business management relies to some extent on the monitoring and analysis of collected data. Smart manufacturing, a vital aspect of Industry 4.0, includes elements such as processes, human/machine interactions, and the transition from paper to digital data. The primary objective is to establish a comprehensive digital interaction mechanism covering human-to-human, human-to-object, and object-to-object communication throughout the entire production process [9]. 3. Methodology The purpose of the model This study proposes an innovative approach to integrate Industry 4.0 technologies with robust data reliability management practices, aiming to elevate productivity in Small and Medium-sized Enterprises (SMEs), with a specific focus on the plastics industry. The research explores the synergies between smart manufacturing processes and advanced data management strategies, emphasizing the pivotal role of reliable data in optimizing operations and fostering sustainable growth. The proposed model is based on 7 pillars: Feedback Loop Establishment : Establishing a closed-loop system that actively incorporates data insights into the decision-making process. Designing a feedback mechanism where real-time data from production processes informs decision-makers about current performance, enabling prompt adjustments and improvements. Iterative Process Refinement : Cultivating a culture of continuous improvement by using data feedback to iteratively refine manufacturing processes. Employee Involvement in Improvement : Fostering a culture where employees at all levels actively contribute insights and suggestions for process improvement. Key Performance Indicator (KPI) Alignment : Ensuring that KPIs align with the overarching business goals and are regularly updated based on data insights. Real-time Monitoring and decision making : Infrastructure: Implementing tools for real-time monitoring of production processes and generating automated reports. Data-Driven Innovation : Encouraging innovation initiatives that emerge from data insights, promoting a proactive approach to product and process enhancements. Continuous Learning and Adaptation : Instilling a culture of continuous learning, where employees are encouraged to adapt and learn from both successes and challenges identified through data. The proposed model for operational performance using OEE The model introduced in this section serves as a global framework. It will be used for its application in the context of productivity optimization with the OEE indicator. The sub-model proposed in the Fig. 1 represents a specific integration productivity enhancement. 4. Case Study In this case study, we examine a Small and Medium-sized Enterprise (SME) operating in the plastic industry, grappling with significant challenges in industrial performance primarily linked to organizational deficiencies. The current state of the production system reflects suboptimal performance, prompting the need for the proposed model to address these issues. The application focuses on restructuring organizational workflows (data collecting and analysis), implementing effective strategies to enhance operational performance, and optimizing resource allocation. Drawing insights from contemporary management methodologies, especially OEE and Total Productive Manufacturing and Industry 4.0 techniques, we aim to enhance value creation, streamline processes, and minimize non-value-added operations. Data collection and decision-making process The data filled in the manufacturing sheet SH1 by the production teams includes finished product references, their quantity and some machine parameters. The quality department records on the daily form (SH3) the number of non-conforming products, as well as parameters such as the weight and size of these products. The finished product storage warehouse, on its part, records the quantity of finished products and the product exits from the warehouse (sheet SH3). Figure 2 illustrates the flow of data collection in the process. In order to demonstrate the impact of poor organization on productivity outcomes, we will measure the OEE ratio for the four months preceding the implementation of the model. Below, Fig. 3 illustrates the trend of OEE and its low value recorded despite the efforts of the production system. Data Reliability, OEE Calculation, and Their Impact on Decision-Making Incomplete data, whether resulting from incomplete collection or insufficient input, poses a significant challenge in the context of analysis and decision-making. The accuracy of manually collected data is often prone to human errors, compromising the reliability of the information. Furthermore, the presence of multiple sources of data to calculate the same indicators can lead to inconsistencies and discrepancies in the results. The absence of a reliable database also represents a significant obstacle. A robust infrastructure is essential to ensure the consistency and integrity of data, and its absence can result in gaps in the overall understanding of available information. Additionally, dependence on claims and the urgent need for data verification add a dimension of time pressure, further compromising the quality and truthfulness of the gathered information. Thus, managing and improving data quality become crucial aspects to ensure a solid foundation for any analysis or decision-making process. Implementation of the proposed model After studying the problem in the production lines and to enhance operational efficiency, several strategic initiatives has been proposed. First, the transition from traditional data entry on sheets to the utilization of calculation tablets and digital files is recommended (Fig. 4). This shift aims to streamline processes and eliminate manual data entry errors. Additionally, the calculation of Overall Equipment Effectiveness (OEE) is suggested on a shift-wise basis, as opposed to the conventional monthly assessment. This real-time approach provides more accurate insights into production efficiency and enables prompt corrective actions. Figure 4. transition from traditional data entry on sheets to online tablets data entry Furthermore, a comprehensive training program on OEE calculation is proposed, emphasizing its direct impact on productivity. This program will not only educate employees but also equip them with the knowledge needed to implement effective improvement strategies. Creating sub-indicators for availability, performance, and quality specific to each department ensures a targeted approach to addressing key operational aspects. Integrating frontline staff—operators, maintenance technicians, and quality agents—into weekly OEE tracking and action implementation meetings fosters collaboration and collective responsibility. To encourage a culture of continuous improvement, the establishment of idea boxes and incentive programs is suggested, encouraging operators and collaborators to contribute innovative ideas for productivity enhancement. Lastly, implementing semi-annual productivity review sessions, involving external training and discussions with industry experts, creates a forum for ongoing learning and strategic planning outside the day-to-day operational environment. Table 1 resumes all those established actions. After several meetings with the plant director and fieldwork sessions, it was decided to implement actions following pillar 1 and 2, based on the established model using OEE, a strategy involving data collection by operators from each department. The analysis and verification of this data will be carried out by process managers (quality, maintenance manager, etc.). Only urgent actions will be initiated, if necessary. The Overall Equipment Effectiveness (OEE) calculation is performed by the calculation department, and a weekly action plan is established during a brief meeting. A more comprehensive action plan is formulated monthly and semi-annually in the presence of the plant director (see Fig. 5 ). Table 1 Applied actions in the plant using the proposed model Pillar Applied actions Pillar 1 OEE Feedback Loop Establishment and data reliability insurance ✓ Assign calculation tablets and digital files and eliminate data entry on sheets. Introduce a shift-wise calculation of OEE (Fig. 4) Pillar 2 Iterative OEE Refinement ✓ Review the OEE tracking on a daily and weekly basis instead of reviewing it monthly Pillar 3 Employee Involvement in OEE Improvement ✓ Make a training program on OEE calculation, its impact on productivity, and the actions to implement for improvement Pillar 4 KPI Alignment with OEE ✓ Creation of sub-indicators for availability, performance, and quality, respectively, for the maintenance department, quality department, and production department. Pillar 5 OEE-Driven Decision-Making Culture ✓ Integrate operators, maintenance technicians, and quality agents into the weekly OEE tracking and action implementation meetings (quick meetings) Pillar 6 OEE-Driven productivity enhancement ✓ Establish idea boxes and incentives to encourage operators and collaborators to propose new ideas for improving productivity Pillar 7 Continuous Learning and Adaptation ✓ Implement a semi-annual productivity review session involving a training and discussion cycle outside the company, within training program with experts The pillar 4, it has been introduced 11 KPI (see Table 2 ), such as the number of conforming products and the product weight relative to the target, assess adherence to established standards. Performance indicators, including MTBF, MTTF, availability, and changeover time, provide insights into equipment reliability and operational efficiency. Human performance metrics, like machine rate per operator and corrective maintenance time, evaluate the effectiveness of workforce contributions to overall operational excellence. Table 2 Sub indicators introduced in the pillar 4 KPI : Quality Unit Number Conforming Product Nb Units Product weight / Target % % Waste (Tn) Number of Non-Conforming Product (ppm) Performance: MTBF (Mean Time Between Failure) (Hour) MTTF (Mean Time To Failure) (Hour) Availability (%) Change Over Time (Hour) Human Performance Machine Rate / operator (Units/Hour) Time of Corrective Maintenance / Maintenance Agent (Hour) PPM/ operator (ppm) Results Through the model proposed by our research team and the actions deployed in the field, along with the commitment of both management and operators, it was possible to make this proposed model feasible in the workshop. Significant improvements were achieved in the productivity of the studied production lines. It is important to note that these improvements are not temporary but sustainable, as we addressed the root causes within the organization by addressing issues related to data collection, analysis, and Overall Equipment Effectiveness (OEE) calculation. The OEE has been improved by several percentages since the information was gathered quickly and efficiently. Figure 6 shows the discussed improvement. The company's profits have increased, prompting decision-makers to consider further leveraging Industry 4.0 tools in automating performance tracking and expanding these efforts to other production lines. 5. Conclusion The proposed model in this case study is underpinned by two fundamental elements crucial for measuring productivity within a Small and Medium-sized Enterprise (SME). First, it delves into the significance of input data for calculating OEE and explores strategies for leveraging this key performance indicator to enhance overall operational efficiency. Given that OEE is contingent on three primary factors, quality, performance, and availability of production equipment. The model advocates for the introduction of automated measures in the context of automated machinery. In instances where the implementation of automatic measures proves unfeasible, the model suggests a pragmatic alternative: the segregation of measures according to the pertinent departments. For instance, the quality factor is recommended to be overseen by the quality management function, and breakdown incidents are advised to be recorded by the production department rather than the maintenance unit. This approach ensures a more targeted and department-specific response to the multifaceted components influencing OEE. Furthermore, the model embraces a comprehensive process-oriented approach. It emphasizes the integration of streamlined processes to enhance the overall efficiency of the production system. By fostering an automated or departmentally segregated approach to address different facets of OEE, the model envisions a holistic strategy that not only refines the measurement of productivity but also systematically enhances the performance of the SME. This forward-thinking methodology aims to fortify the SME against operational challenges and position it on a trajectory of sustained growth and competitiveness. Declarations Competing Interest: Authors of this paper have no financial interests and have no relevant financial or non-financial interests to disclose.” Funding: The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Author Contribution All authors contributed to this work. Data availability statements Data used in the research paper are confidential and represent crucial production and maintenance data of a leading company in plastics manufacturing. For further information on this subject, please contact Hassan II University (ENSAM Casablanca) or directly the corresponding author of this article to obtain a detailed overview of the data structure. References D. Klimecka-Tatar, M. Ingaldia, Digitization of processes in manufacturing SMEs - value stream mapping and OEE analysis, Procedia Computer Science 200, pages 660–668, 2022. H. Kagermann, W. Wahlster, and J. Helbig, Recommendations for implementing the strategicinitiative Industrie 4.0–final report of the Industrie 4.0 working group. Communication Promoters Group of the Industry, Frankfurt, 2013. J.M. Müller, Business model innovation in small- and medium-sized enterprises, Journal of Manufacturing Technology Management 30 Vol. 8, pages 1127–1142, 2019. E. Rauch, M. Unterhofer, R. Rojas, L. Gualtieri, M. Woschank, and D. T. Matt, A Maturity Level-Based Assessment Tool to Enhance the Implementation of Industry 4.0 in Small and Medium-Sized Enterprises, Sustainability 12 (9), 3559, 2020. W. Nußer, T. Steckel, A mathematical model for the determination of performance losses of machines. Applied Mathematical Modelling 92, 612–623, 2021. X. Li, G. Liu, X. Hao, Research on improved oee measurement method based on the multiproduct production system. Applied Sciences,11(2), 490, 2021. M. Perez-Perez, A.M. Serrano Bedia, M. C. Lopez-Fernandez, G. Garcia Piqueres, Research opportunities on manufacturing flexibility domain: A review and theory-based research agenda, Journal of Manufacturing Systems, 48, 9–20, 2021. D. Klimecka-Tatara, M. Ingaldi, Digitization of processes in manufacturing SMEs - value stream mapping and OEE analysis, 3rd International Conference on Industry 4.0 and Smart Manufacturing, Procedia Computer Science 200, 660–668, 2022. H. Kagermann, W. Wahlster, and J. Helbig, Recommendations for implementing the strategicinitiative Industrie 4.0–final report of the Industrie 4.0 working group. Communication Promoters Group of the Industry, Frankfurt, 2013. V. Cirillo, J. Molero Zayas, Digitalizing industry? Labor, technology and work organization: an introduction to the Forum, Journal of Industrial and Business Economics, Volume 46, pages 313–321, 2019. V. Cirillo, V., M. Rinaldini, J. Staccioli, M. E. Virgillito, Workers’ intervention authority in Italian 4.0 factories: Autonomy and discretion, Laboratory of Economics and Management (LEM) Italy, 2018. B. Harrison, B., Lean and mean: The changing landscape of corporate power in the age of flexibility. New York: Basic Books. https://doi.org/10.2307/2524930, 1994. M. Alvesson, M., S. Sveningsson, Good visions, bad micro-management and ugly ambiguity: Contradictions of (non-) leadership in a knowledge-intensive organization. Organization Studies, 24(6), 961–988, 2003. S. Zuboff, Big other: Surveillance capitalism and the prospects of an information civilization. Journal of Information Technology, 30(1), 75–89, 2015. S. P. Choudary, The architecture of digital labour platforms: Policy recommendations on platform design for worker well-being. ILO Future of work Research Paper Series, ISBN 978-92-2-030770-0, 2018. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3893505","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":270413430,"identity":"24192ac9-4797-45af-b876-33c3b9dd89ea","order_by":0,"name":"Meddaoui 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Introduction","content":"\u003cp\u003eThe advent of Industry 4.0 marks a revolutionary paradigm shift, integrating cutting-edge technologies into manufacturing processes, thereby propelling industries towards heightened efficiency and competitiveness. Concurrently, the Overall Equipment Effectiveness (OEE) metric emerges as a vital tool, serving as a compass for enterprises seeking to optimize their machinery performance and operational processes. Additionally, recognizing the crucial role played by industrial data in the modern business ecosystem, the efficient harnessing and utilization of digital information in SMEs stand as a cornerstone for informed decision-making and improved organizational efficacy. This exploration delves into the synergy between Industry 4.0, OEE, and the efficient management of data, unraveling the transformative potential they collectively hold for the thriving SME sector.\u003c/p\u003e \u003cp\u003eOtherwise, this fourth revolution and the resulting changes in process management have a very significant impact on the meaning and method of obtaining digital data. The principles of Industry 4.0 are already based on transparency and clarity of data throughout the process. Additionally, the assumptions of modern management methods (Lean) influence the need for changes in creating value by minimizing losses in operational areas that do not add value and ensuring transparency in operations that do add value. It seems particularly challenging to introduce changes (in line with 21st-century trends) in small and medium-sized enterprises (SMEs) in the manufacturing sector, as SMEs show a low level of maturity in the field of management [1].\u003c/p\u003e \u003cp\u003eOur article aims to deepen our understanding of the central role played by productivity measurement within small and medium-sized enterprises (SMEs). We strive to comprehensively break down the impact of data reliability on the accurate assessment of operational performance within these entrepreneurial structures. A prominent aspect of this research involves exploring how the innovative tools offered by Industry 4.0 can be judiciously utilized to enhance the reliability of data stemming from various processes such as production, quality, and maintenance. In this context, we delve into the design and implementation of a comprehensive model for data collection, data reliability, and data utilization. The ultimate goal of this model is to establish a robust methodology for improving Overall Equipment Effectiveness (OEE) within SMEs. We aim to demonstrate how Industry 4.0 advancements can be synergistically integrated into these processes to ensure increased data quality and reliability. By exploring these innovative avenues, we aspire to provide SME stakeholders with practical insights and informed recommendations on how to best leverage Industry 4.0 technologies to optimize their operational performance. In summary, our approach is geared towards making tangible contributions to the understanding and successful implementation of efficient methodologies for data collection, reliability, and utilization, thereby contributing to the sustainable improvement of OEE within SMEs.\u003c/p\u003e"},{"header":"2. Literature Review","content":" \u003cp\u003e \u003cb\u003eIndustry 4.0 Dynamics\u003c/b\u003e \u003c/p\u003e\u003cp\u003eIndustry 4.0 sets the stage for a comprehensive societal and technological transformation, reshaping the global landscape significantly. Information is seamlessly integrated into the components, allowing for tasks such as ordering missing parts and configuring individual production parameters. Concurrently, clients are continuously informed about the latest production status. As the plant commences operations, a wealth of additional data is generated. Precise output and real-time performance data of the products can be collected, analyzed, and fed back into the development process. In this context, Industry 4.0 technologies play a pivotal role in advancing and optimizing both new technologies and processes.\u003c/p\u003e \u003cp\u003eIn Industry 4.0, business management relies, to some extent, on monitoring and analyzing collected data. Key components of smart manufacturing include processes, human/machine interactions, and the transition from paper to digital data. The primary objective is to establish a digital interaction mechanism for human-to-human, human-to-object, and object-to-object communication throughout the entire production process [2].\u003c/p\u003e \u003cp\u003eOther researchers present a model application tailored for small and medium-sized enterprises (SMEs), providing a comprehensive overview of existing Industry 4.0 concepts. Concurrently, M\u0026uuml;ller suggests associating business model implications with Industry 4.0, utilizing the Business Model Canvas as a reference. Elements such as key resources and value propositions are identified as the most significantly influenced components of the business model, while channels are noted as being less affected [3]. Rauch et al. model supports SMEs in formulating an individual strategy for the successful implementation of Industry 4.0 [4].\u003c/p\u003e \u003cp\u003eIn today's manufacturing landscape, production companies face a crucial mandate for both operational excellence and flexibility in their manufacturing and assembly operations. This imperative arises from the ongoing shift from mass production to mass customization [5]. The evaluation of industrial system effectiveness, encompassing processes and machinery, has a well-established history and remains a central focus in recent research [6]. Considerable attention has also been devoted to exploring the flexibility of manufacturing equipment and its interconnectedness in achieving overall manufacturing flexibility. Current research is dedicated to clarifying concepts, operationalization, measurement frameworks, and related aspects [7].\u003c/p\u003e \u003cp\u003e \u003cb\u003eChallenges and Opportunities\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe discourse surrounding \"Industry 4.0\" and the broader digitization process revolves around internal discussions concerning the technological challenges and opportunities presented by recent advancements. There is also significant consideration given to the direct and indirect impacts on employment, encompassing both quantitative and qualitative aspects, as well as on labor conditions. In recent years, two distinct narratives have surfaced. From a firm-level perspective and grounded in managerial discussions, the narrative of \"emerging opportunities\" suggests that digitalization offers new possibilities for companies. This perspective envisions firms becoming more agile and intelligent, reducing inefficiencies, fostering collaborative working systems, and optimizing inter-organizational relations within what is termed 'industrial ecosystems' [10]. As emphasized by Cirillo et al., an opposing perspective arises from a reading that underscores the risks associated with the widespread digitalization and interconnection of processes [11]. These risks include the reinforcement of decision-making power without the centralization of production [12], the resurgence of neo-Taylorization in work processes through the introduction of micromanagement practices and new forms of proceduralization characterized by extensive surveillance systems [13,14], and the unequal distribution of power and information [15].\u003c/p\u003e \u003cp\u003e \u003cb\u003eIndustry 4.0: Data Transformation Challenges\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe emergence of Industry 4.0 and its consequential changes in process management significantly impact the interpretation and utilization of digital data. The principles of Industry 4.0 are inherently grounded in ensuring transparency and clarity of data throughout the entire process. Additionally, contemporary management methodologies, such as Lean principles, contribute to the imperative of transforming the value creation process. This transformation involves minimizing losses in non-value-added operations and enhancing the transparency of operations that contribute value. Implementing these changes, in line with 21st-century trends, presents a specific challenge for small and medium-sized enterprises (SMEs) in the manufacturing sector. This challenge is exacerbated by the relatively low level of managerial maturity exhibited by SMEs in this domain [8].\u003c/p\u003e \u003cp\u003eThe adoption of innovative technologies presents a challenge for small and medium-sized enterprises (SMEs), given their inherent weakness in handling complex procedures. This challenge is particularly evident in the context of Industry 4.0, where business management relies to some extent on the monitoring and analysis of collected data. Smart manufacturing, a vital aspect of Industry 4.0, includes elements such as processes, human/machine interactions, and the transition from paper to digital data. The primary objective is to establish a comprehensive digital interaction mechanism covering human-to-human, human-to-object, and object-to-object communication throughout the entire production process [9].\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eThe purpose of the model\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThis study proposes an innovative approach to integrate Industry 4.0 technologies with robust data reliability management practices, aiming to elevate productivity in Small and Medium-sized Enterprises (SMEs), with a specific focus on the plastics industry. The research explores the synergies between smart manufacturing processes and advanced data management strategies, emphasizing the pivotal role of reliable data in optimizing operations and fostering sustainable growth.\u003c/p\u003e \u003cp\u003eThe proposed model is based on 7 pillars:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eFeedback Loop Establishment\u003c/b\u003e: Establishing a closed-loop system that actively incorporates data insights into the decision-making process. Designing a feedback mechanism where real-time data from production processes informs decision-makers about current performance, enabling prompt adjustments and improvements.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eIterative Process Refinement\u003c/b\u003e: Cultivating a culture of continuous improvement by using data feedback to iteratively refine manufacturing processes.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eEmployee Involvement in Improvement\u003c/b\u003e: Fostering a culture where employees at all levels actively contribute insights and suggestions for process improvement.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eKey Performance Indicator (KPI) Alignment\u003c/b\u003e: Ensuring that KPIs align with the overarching business goals and are regularly updated based on data insights.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eReal-time Monitoring and decision making\u003c/b\u003e: Infrastructure: Implementing tools for real-time monitoring of production processes and generating automated reports.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eData-Driven Innovation\u003c/b\u003e: Encouraging innovation initiatives that emerge from data insights, promoting a proactive approach to product and process enhancements.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eContinuous Learning and Adaptation\u003c/b\u003e: Instilling a culture of continuous learning, where employees are encouraged to adapt and learn from both successes and challenges identified through data.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eThe proposed model for operational performance using OEE\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe model introduced in this section serves as a global framework. It will be used for its application in the context of productivity optimization with the OEE indicator. The sub-model proposed in the Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e represents a specific integration productivity enhancement.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"4. Case Study","content":"\u003cp\u003eIn this case study, we examine a Small and Medium-sized Enterprise (SME) operating in the plastic industry, grappling with significant challenges in industrial performance primarily linked to organizational deficiencies. The current state of the production system reflects suboptimal performance, prompting the need for the proposed model to address these issues. The application focuses on restructuring organizational workflows (data collecting and analysis), implementing effective strategies to enhance operational performance, and optimizing resource allocation. Drawing insights from contemporary management methodologies, especially OEE and Total Productive Manufacturing and Industry 4.0 techniques, we aim to enhance value creation, streamline processes, and minimize non-value-added operations.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eData collection and decision-making process\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe data filled in the manufacturing sheet SH1 by the production teams includes finished product references, their quantity and some machine parameters. The quality department records on the daily form (SH3) the number of non-conforming products, as well as parameters such as the weight and size of these products. The finished product storage warehouse, on its part, records the quantity of finished products and the product exits from the warehouse (sheet SH3). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the flow of data collection in the process.\u003c/p\u003e \u003cp\u003eIn order to demonstrate the impact of poor organization on productivity outcomes, we will measure the OEE ratio for the four months preceding the implementation of the model. Below, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the trend of OEE and its low value recorded despite the efforts of the production system.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eData Reliability, OEE Calculation, and Their Impact on Decision-Making\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eIncomplete data, whether resulting from incomplete collection or insufficient input, poses a significant challenge in the context of analysis and decision-making. The accuracy of manually collected data is often prone to human errors, compromising the reliability of the information. Furthermore, the presence of multiple sources of data to calculate the same indicators can lead to inconsistencies and discrepancies in the results.\u003c/p\u003e \u003cp\u003eThe absence of a reliable database also represents a significant obstacle.\u003c/p\u003e \u003cp\u003eA robust infrastructure is essential to ensure the consistency and integrity of data, and its absence can result in gaps in the overall understanding of available information. Additionally, dependence on claims and the urgent need for data verification add a dimension of time pressure, further compromising the quality and truthfulness of the gathered information. Thus, managing and improving data quality become crucial aspects to ensure a solid foundation for any analysis or decision-making process.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eImplementation of the proposed model\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eAfter studying the problem in the production lines and to enhance operational efficiency, several strategic initiatives has been proposed. First, the transition from traditional data entry on sheets to the utilization of calculation tablets and digital files is recommended (Fig.\u0026nbsp;4). This shift aims to streamline processes and eliminate manual data entry errors. Additionally, the calculation of Overall Equipment Effectiveness (OEE) is suggested on a shift-wise basis, as opposed to the conventional monthly assessment. This real-time approach provides more accurate insights into production efficiency and enables prompt corrective actions.\u003c/p\u003e \u003cp\u003eFigure 4. transition from traditional data entry on sheets to online tablets data entry\u003c/p\u003e \u003cp\u003eFurthermore, a comprehensive training program on OEE calculation is proposed, emphasizing its direct impact on productivity. This program will not only educate employees but also equip them with the knowledge needed to implement effective improvement strategies. Creating sub-indicators for availability, performance, and quality specific to each department ensures a targeted approach to addressing key operational aspects. Integrating frontline staff\u0026mdash;operators, maintenance technicians, and quality agents\u0026mdash;into weekly OEE tracking and action implementation meetings fosters collaboration and collective responsibility. To encourage a culture of continuous improvement, the establishment of idea boxes and incentive programs is suggested, encouraging operators and collaborators to contribute innovative ideas for productivity enhancement. Lastly, implementing semi-annual productivity review sessions, involving external training and discussions with industry experts, creates a forum for ongoing learning and strategic planning outside the day-to-day operational environment. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e resumes all those established actions.\u003c/p\u003e \u003cp\u003eAfter several meetings with the plant director and fieldwork sessions, it was decided to implement actions following pillar 1 and 2, based on the established model using OEE, a strategy involving data collection by operators from each department. The analysis and verification of this data will be carried out by process managers (quality, maintenance manager, etc.). Only urgent actions will be initiated, if necessary. The Overall Equipment Effectiveness (OEE) calculation is performed by the calculation department, and a weekly action plan is established during a brief meeting. A more comprehensive action plan is formulated monthly and semi-annually in the presence of the plant director (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eApplied actions in the plant using the proposed model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePillar\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eApplied actions\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePillar 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOEE Feedback Loop Establishment and data reliability insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e✓ Assign calculation tablets and digital files and eliminate data entry on sheets. Introduce a shift-wise calculation of OEE (Fig.\u0026nbsp;4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePillar 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIterative OEE Refinement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e✓ Review the OEE tracking on a daily and weekly basis instead of reviewing it monthly\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePillar 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmployee Involvement in OEE Improvement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e✓ Make a training program on OEE calculation, its impact on productivity, and the actions to implement for improvement\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePillar 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKPI Alignment with OEE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e✓ Creation of sub-indicators for availability, performance, and quality, respectively, for the maintenance department, quality department, and production department.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePillar 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOEE-Driven Decision-Making Culture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e✓ Integrate operators, maintenance technicians, and quality agents into the weekly OEE tracking and action implementation meetings (quick meetings)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePillar 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOEE-Driven productivity enhancement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e✓ Establish idea boxes and incentives to encourage operators and collaborators to propose new ideas for improving productivity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePillar 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eContinuous Learning and Adaptation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e✓ Implement a semi-annual productivity review session involving a training and discussion cycle outside the company, within training program with experts\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe pillar 4, it has been introduced 11 KPI (see Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), such as the number of conforming products and the product weight relative to the target, assess adherence to established standards. Performance indicators, including MTBF, MTTF, availability, and changeover time, provide insights into equipment reliability and operational efficiency. Human performance metrics, like machine rate per operator and corrective maintenance time, evaluate the effectiveness of workforce contributions to overall operational excellence.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSub indicators introduced in the pillar 4\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eKPI :\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eQuality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber Conforming Product\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNb Units\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProduct weight / Target\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Waste\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Tn)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of Non-Conforming Product\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(ppm)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePerformance:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMTBF (Mean Time Between Failure)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Hour)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMTTF (Mean Time To Failure)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Hour)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAvailability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChange Over Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Hour)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHuman Performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMachine Rate / operator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Units/Hour)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTime of Corrective Maintenance / Maintenance Agent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Hour)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePPM/ operator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(ppm)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eResults\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThrough the model proposed by our research team and the actions deployed in the field, along with the commitment of both management and operators, it was possible to make this proposed model feasible in the workshop. Significant improvements were achieved in the productivity of the studied production lines. It is important to note that these improvements are not temporary but sustainable, as we addressed the root causes within the organization by addressing issues related to data collection, analysis, and Overall Equipment Effectiveness (OEE) calculation. The OEE has been improved by several percentages since the information was gathered quickly and efficiently. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the discussed improvement.\u003c/p\u003e \u003cp\u003eThe company's profits have increased, prompting decision-makers to consider further leveraging Industry 4.0 tools in automating performance tracking and expanding these efforts to other production lines.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe proposed model in this case study is underpinned by two fundamental elements crucial for measuring productivity within a Small and Medium-sized Enterprise (SME). First, it delves into the significance of input data for calculating OEE and explores strategies for leveraging this key performance indicator to enhance overall operational efficiency. Given that OEE is contingent on three primary factors, quality, performance, and availability of production equipment. The model advocates for the introduction of automated measures in the context of automated machinery. In instances where the implementation of automatic measures proves unfeasible, the model suggests a pragmatic alternative: the segregation of measures according to the pertinent departments. For instance, the quality factor is recommended to be overseen by the quality management function, and breakdown incidents are advised to be recorded by the production department rather than the maintenance unit. This approach ensures a more targeted and department-specific response to the multifaceted components influencing OEE.\u003c/p\u003e \u003cp\u003eFurthermore, the model embraces a comprehensive process-oriented approach. It emphasizes the integration of streamlined processes to enhance the overall efficiency of the production system. By fostering an automated or departmentally segregated approach to address different facets of OEE, the model envisions a holistic strategy that not only refines the measurement of productivity but also systematically enhances the performance of the SME. This forward-thinking methodology aims to fortify the SME against operational challenges and position it on a trajectory of sustained growth and competitiveness.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interest:\u003c/h2\u003e \u003cp\u003eAuthors of this paper have no financial interests and have no relevant financial or non-financial interests to disclose.\u0026rdquo;\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to this work.\u003c/p\u003e\u003ch2\u003eData availability statements\u003c/h2\u003e \u003cp\u003eData used in the research paper are confidential and represent crucial production and maintenance data of a leading company in plastics manufacturing. For further information on this subject, please contact Hassan II University (ENSAM Casablanca) or directly the corresponding author of this article to obtain a detailed overview of the data structure.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eD. Klimecka-Tatar, M. Ingaldia, Digitization of processes in manufacturing SMEs - value stream mapping and OEE analysis, Procedia Computer Science 200, pages 660\u0026ndash;668, 2022.\u003c/li\u003e\n\u003cli\u003eH. Kagermann, W. Wahlster, and J. Helbig, Recommendations for implementing the strategicinitiative Industrie 4.0\u0026ndash;final report of the Industrie 4.0 working group. Communication Promoters Group of the Industry, Frankfurt, 2013.\u003c/li\u003e\n\u003cli\u003eJ.M. M\u0026uuml;ller, Business model innovation in small- and medium-sized enterprises, Journal of Manufacturing Technology Management 30 Vol. 8, pages 1127\u0026ndash;1142, 2019.\u003c/li\u003e\n\u003cli\u003eE. Rauch, M. Unterhofer, R. Rojas, L. Gualtieri, M. Woschank, and D. T. Matt, A Maturity Level-Based Assessment Tool to Enhance the Implementation of Industry 4.0 in Small and Medium-Sized Enterprises, Sustainability 12 (9), 3559, 2020.\u003c/li\u003e\n\u003cli\u003eW. Nu\u0026szlig;er, T. Steckel, A mathematical model for the determination of performance losses of machines. Applied Mathematical Modelling 92, 612\u0026ndash;623, 2021.\u003c/li\u003e\n\u003cli\u003eX. Li, G. Liu, X. Hao, Research on improved oee measurement method based on the multiproduct production system. Applied Sciences,11(2), 490, 2021.\u003c/li\u003e\n\u003cli\u003eM. Perez-Perez, A.M. Serrano Bedia, M. C. Lopez-Fernandez, G. Garcia Piqueres, Research opportunities on manufacturing flexibility domain: A review and theory-based research agenda, Journal of Manufacturing Systems, 48, 9\u0026ndash;20, 2021.\u003c/li\u003e\n\u003cli\u003eD. Klimecka-Tatara, M. Ingaldi, Digitization of processes in manufacturing SMEs - value stream mapping and OEE analysis, 3rd International Conference on Industry 4.0 and Smart Manufacturing, Procedia Computer Science 200, 660\u0026ndash;668, 2022.\u003c/li\u003e\n\u003cli\u003eH. Kagermann, W. Wahlster, and J. Helbig, Recommendations for implementing the strategicinitiative Industrie 4.0\u0026ndash;final report of the Industrie 4.0 working group. Communication Promoters Group of the Industry, Frankfurt, 2013.\u003c/li\u003e\n\u003cli\u003eV. Cirillo, J. Molero Zayas, Digitalizing industry? Labor, technology and work organization: an introduction to the Forum, Journal of Industrial and Business Economics, Volume 46, pages 313\u0026ndash;321, 2019.\u003c/li\u003e\n\u003cli\u003eV. Cirillo, V., M. Rinaldini, J. Staccioli, M. E. Virgillito, Workers\u0026rsquo; intervention authority in Italian 4.0 factories: Autonomy and discretion, Laboratory of Economics and Management (LEM) Italy, 2018.\u003c/li\u003e\n\u003cli\u003eB. Harrison, B., Lean and mean: The changing landscape of corporate power in the age of flexibility. New York: Basic Books. https://doi.org/10.2307/2524930, 1994.\u003c/li\u003e\n\u003cli\u003eM. Alvesson, M., S. Sveningsson, Good visions, bad micro-management and ugly ambiguity: Contradictions of (non-) leadership in a knowledge-intensive organization. Organization Studies, 24(6), 961\u0026ndash;988, 2003.\u003c/li\u003e\n\u003cli\u003eS. Zuboff, Big other: Surveillance capitalism and the prospects of an information civilization. Journal of Information Technology, 30(1), 75\u0026ndash;89, 2015.\u003c/li\u003e\n\u003cli\u003eS. P. Choudary, The architecture of digital labour platforms: Policy recommendations on platform design for worker well-being. ILO Future of work Research Paper Series, ISBN 978-92-2-030770-0, 2018.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Industry 4.0, SMEs, OEE, Industrial Performance, Plastics Industry","lastPublishedDoi":"10.21203/rs.3.rs-3893505/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3893505/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn today's industrial context, three key elements are guiding the course of small and medium-sized enterprises (SMEs) towards improved productivity, efficient operations, and sustainable growth. The introduction of Industry 4.0 signifies a groundbreaking shift, integrating state-of-the-art technologies into manufacturing processes and propelling industries towards heightened efficiency and competitiveness. This article deals with the crucial role of productivity measurement in SMEs and examines the impact of data reliability on operational performance assessment. It explores the strategic use of Industry 4.0 tools to enhance data reliability in processes like production, quality, and maintenance. The research focuses on designing a comprehensive model for data collection, reliability, and utilization, ultimately aiming to improve Overall Equipment Effectiveness (OEE) within SMEs. By showcasing the synergistic integration of Industry 4.0 advancements, the article provides practical insights for SME stakeholders to optimize operational performance. The proposed model contributes to the understanding and implementation of efficient methodologies for data management, fostering sustainable improvements using calculation of OEE within SMEs. The case study was conducted in a plastics manufacturing SME that produces components for various industries. These findings can be enhanced and improved through additional case studies to refine the proposed model.\u003c/p\u003e","manuscriptTitle":"Optimizing operational performance through industry 4.0: a comprehensive model for data reliability and OEE enhancement","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-02 15:27:24","doi":"10.21203/rs.3.rs-3893505/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7d299d89-c890-496e-9200-f3878975084b","owner":[],"postedDate":"February 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-12T10:30:22+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-02 15:27:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3893505","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3893505","identity":"rs-3893505","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}